Who are we wrt R?

Wherever you are, you’re not alone! As we begin learning R (or learning new things in R), remember…

R is the computational engine; RStudio is the interface

Projects allow RStudio to leave notes for itself (e.g., history), will always start a new R session when opened, and will always set the working directory to the Project directory.

Create a system for organizing the objects in this project!

Functions are the “verbs” that allow us to manipulate data. Packages contain functions, and all functions belong to packages.

R comes with about 30 packages (“base R”). There are over 10,000 user-contributed packages; you can discover these packages online in Comprehensive R Archive Network (CRAN), with more in active development on GitHub.

To use a package, install it once

• You can install packages via point-and-click: Tools…Install Packages…Enter tidyverse (or a different package name) then click on Install.
• Or you can use this command in the console: install.packages("tidyverse")

In each new R session, you’ll have to load the package if you want access to its functions: e.g., type library(tidyverse).

• R is case sensitive
• Everything in R is an object (vectors, lists, matrices, data frames)
• # demarcates code comments
• <- is the assignment operator, how we name new objects in the R environment

R has two native data formats:

• .RDS files store a single data object: readRDS("path/filename.RDS"), saveRDS(object, file = "path/filename.RDS")
• .RData files stores multiple data objects: load("path/filename.Rdata"), save(object1, object2, file = "path/filename.RData"), save.image("path/filename.RData")

You can import any data format if you know the right command/(package):

• CSV: read.csv (base R), read_csv (tidyverse)
• Excel: read_excel (readxl)
• Stata, SPSS, SAS: e.g., read.dta (foreign), read_dta (haven)
• JSON, fixed-width, TXT, DAT, shape files, etc.

Primary data types include numeric, integer, logical, and character; plus factors.

Download R materials from today’s canvas page!

Artwork by @allison_horst

Examining data:

• names()
• head() and tail()
• str(); glimpse() (dplyr equivalent)
• summary()

These (base R) commands will operate an the full object (all variables/columns in a data frame). To access a specific variable/column, use the $ operator: df$varname.

Part of the the tidyverse, dplyr is a package for data manipulation. The package implements a grammar for transforming data, based on verbs/functions that define a set of common tasks.

dplyr functions are for data frames.

• first argument of dplyr functions is always a data frame
• followed by function specific arguments that detail what to do

dplyr cheatsheet!

select(.data, var1, var2, var3)

select() helpers include

• select(.data, var1:var10): select range of columns
• select(.data, -c(var1, var2)): select every column but
• select(.data, starts_with("string")): select columns that start with… (or ends_with(“string”))
• select(.data, contains("string")): select columns whose names contain…

filter(.data, var == value)

arrange() - reorder rows

arrange(.data, var)
arrange(.data, desc(var))

count() - tabulate values of variables

count(.data, var)

The pipe (%>%) allows you to chain together functions by passing (piping) the result on the left into the first argument of the function on the right. It allows us to call a series of functions in sequence (read the pipe as “and then…”).

dataframe %>% 
  filter(var1 > 0) %>% 
  select(var1, var2, var3) 

Keyboard shortcut to create %>%

• Mac: cmd + shift + m
• Windows: ctrl + shift + m